[neunhoef]

Author of posted article here: thanks for the additional pointers. It seems that graphistry excels at visualization. Essentially, your offering confirms the main story of the article: make more out of your (graph) data by extracting it from Hadoop to a different tool.

And obviously, one should use the right tool for the purpose . I think graphistry is a good choice for graph visualization, graph databases like ArangoDB or Neo4j will be good at ad hoc traversals. And multi-model databases like ArangoDB or OrientDB will be good at a wide range of ad hoc queries. Anyway, thanks again for the pointers.

[lmeyerov]

Yep. Maybe the observation is (1) data has gravity -- it was originally in another non-graph-specific DB -- and (2) the graph structure part is normally small. So we indeed see a lot of extraction into easier-to-use systems.

The nuance being... with stuff like data science notebooks and pandas, the people skilled enough to do extraction are also skilled enough that it's easier to just use pandas. The exception is repeat work or when it is for regular analysts. Friendly query languages like Neo4j's Cypher helps there. Not sure what Arango supports... Gremlin? Proprietary?

Graphistry's environment is agnostic, and _not_ a database, so it'd be wrong of me to advocate teams drop their system of record and use just us ;-) We ended up building a visual "playbook" investigation environment to help teams streamline these scenarios. They run visual playbooks against their legacy db (splunk, elastic, sql, ...) for faux-graph queries, or their new graph db for deeper ones (e.g., path queries). So we're more of the system of record + superpowers for your investigations, kind of like a smarter version of what Tableau/Looker do for SQL.